Change-point detection in time-series data by direct density-ratio estimation

Yoshinobu Kawahara, Masashi Sugiyama

Research output: Chapter in Book/Report/Conference proceedingConference contribution

69 Citations (Scopus)

Abstract

Change-point detection is the problem of discovering time points at which properties of time-series data change. This covers a broad range of real-world problems and has been actively discussed in the community of statistics and data mining. In this paper, we present a novel non-parametric approach to detecting the change of probability distributions of sequence data. Our key idea is to estimate the ratio of probability densities, not the probability densities themselves. This formulation allows us to avoid non-parametric density estimation, which is known to be a difficult problem. We provide a change-point detection algorithm based on direct density-ratio estimation that can be computed very efficiently in an online manner. The usefulness of the proposed method is demonstrated through experiments using artificial and real datasets.

Original languageEnglish
Title of host publicationSociety for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics 133
Pages385-396
Number of pages12
Publication statusPublished - Dec 1 2009
Externally publishedYes
Event9th SIAM International Conference on Data Mining 2009, SDM 2009 - Sparks, NV, United States
Duration: Apr 30 2009May 2 2009

Publication series

NameSociety for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics
Volume1

Other

Other9th SIAM International Conference on Data Mining 2009, SDM 2009
CountryUnited States
CitySparks, NV
Period4/30/095/2/09

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Software
  • Applied Mathematics

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    Kawahara, Y., & Sugiyama, M. (2009). Change-point detection in time-series data by direct density-ratio estimation. In Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics 133 (pp. 385-396). (Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics; Vol. 1).